November 21, 2016

Steps to Install TensorFlow with GPU on Windows

I normally use Encog and a self-written learning framework for when I do audio pipeline learning. I’ve been tempted by CNTK and TensorFlow. CNTK uses tools whose license is, sadly, too restrictive. TensorFlow’s ecosystem is more in-line with what I need.

I’m a windows guy, and I can use TensorFlow(TS) via docker. But, I want to use my GPU. I have a CUDA compliant GPU on one of my machines along with Windows 10 and Visual Studio Community. The official readme is designed for VS Pro, not community. The key difference is that VS Community doesn’t officially support TensorFlow 32 bit with CUDA, only 64 bit.

Here’s the steps I’ve figure out so far:

Prerequisites.

You’ll need SWIG, CUDA, the Nvidea NN library for Cuda, Git, CMake, Python3.5 and numpy 1.11. You can use Anaconda to satisfy the python/numpy requirement. Install Anaconda, then conda install numpy in an elevated command prompt. The rest, you’ll have to download installers and install. Oh, and Visual Studio Community 2015. I’ll assume a default install drive of C: I’ve adapted the steps from the official Github here: